{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 Physical GPUs, 1 Logical GPUs\n"
     ]
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "import graphgallery \n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "import os\n",
    "os.environ[\"CUDA_VISIBLE_DEVICES\"] = '1'\n",
    "# Set if memory growth should be enabled for ALL `PhysicalDevice`.\n",
    "graphgallery.set_memory_growth()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.1.2'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.4.0'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graphgallery.__version__"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Load the Datasets\n",
    "+ cora\n",
    "+ citeseer\n",
    "+ pubmed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from graphgallery.data import Planetoid\n",
    "\n",
    "# set `verbose=False` to avoid these printed tables\n",
    "data = Planetoid('cora', root=\"~/GraphData/datasets\", verbose=False)\n",
    "graph = data.graph\n",
    "idx_train, idx_val, idx_test = data.split()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'citeseer', 'cora', 'pubmed'}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.supported_datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "there may be an error: \n",
    "```\n",
    "# UnknownError:  Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above. [[node model_18/lg_convolution/conv1/conv1d (defined at Graphgallery/graphgallery/nn/layers/lgcn.py:109) ]] [Op:__inference_train_on_batch_108066]\n",
    "```\n",
    "You can fix it using:\n",
    "```python\n",
    "graphgallery.set_memory_growth()\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Large dropout rate: 0.8 (>0.5). In TensorFlow 2.x, dropout() uses dropout rate instead of keep_prob. Please ensure that this is intended.\n",
      "WARNING:tensorflow:Large dropout rate: 0.8 (>0.5). In TensorFlow 2.x, dropout() uses dropout rate instead of keep_prob. Please ensure that this is intended.\n",
      "WARNING:tensorflow:Large dropout rate: 0.8 (>0.5). In TensorFlow 2.x, dropout() uses dropout rate instead of keep_prob. Please ensure that this is intended.\n",
      "WARNING:tensorflow:Large dropout rate: 0.8 (>0.5). In TensorFlow 2.x, dropout() uses dropout rate instead of keep_prob. Please ensure that this is intended.\n",
      "WARNING:tensorflow:Large dropout rate: 0.8 (>0.5). In TensorFlow 2.x, dropout() uses dropout rate instead of keep_prob. Please ensure that this is intended.\n",
      "Training...\n",
      "100/100 [==============================] - 13s 132ms/step - loss: 1.4796 - acc: 0.8643 - val_loss: 1.8184 - val_acc: 0.7560 - time: 13.2140\n",
      "Testing...\n",
      "1/1 [==============================] - 0s 364ms/step - test_loss: 1.6544 - test_acc: 0.8310 - time: 0.3641\n",
      "Test loss 1.6544, Test accuracy 83.10%\n"
     ]
    }
   ],
   "source": [
    "from graphgallery.nn.models import LGCN\n",
    "model = LGCN(graph, attr_transform=\"normalize_attr\", device='GPU', seed=123)\n",
    "model.build()\n",
    "# train with validation\n",
    "his = model.train(idx_train, idx_val, verbose=1, epochs=100)\n",
    "# train without validation\n",
    "# his = model.train(idx_train, verbose=1, epochs=100)\n",
    "loss, accuracy = model.test(idx_test)\n",
    "print(f'Test loss {loss:.5}, Test accuracy {accuracy:.2%}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Show model summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"lgcn\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "attr_matrix (InputLayer)        [(None, 1433)]       0                                            \n",
      "__________________________________________________________________________________________________\n",
      "dropout (Dropout)               (None, 1433)         0           attr_matrix[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "adj_matrix (InputLayer)         [(None, None)]       0                                            \n",
      "__________________________________________________________________________________________________\n",
      "dense_convolution (DenseConvolu (None, 32)           45856       dropout[0][0]                    \n",
      "                                                                 adj_matrix[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "top_k_features (Top_k_features) (None, 9, 32)        0           dense_convolution[0][0]          \n",
      "                                                                 adj_matrix[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "lg_convolution (LGConvolution)  (None, 8)            4020        top_k_features[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization (BatchNorma (None, 8)            32          lg_convolution[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "concatenate (Concatenate)       (None, 40)           0           dense_convolution[0][0]          \n",
      "                                                                 batch_normalization[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "top_k_features_1 (Top_k_feature (None, 9, 40)        0           concatenate[0][0]                \n",
      "                                                                 adj_matrix[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "lg_convolution_1 (LGConvolution (None, 8)            5784        top_k_features_1[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_1 (BatchNor (None, 8)            32          lg_convolution_1[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)     (None, 48)           0           concatenate[0][0]                \n",
      "                                                                 batch_normalization_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "dropout_1 (Dropout)             (None, 48)           0           concatenate_1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dense_convolution_1 (DenseConvo (None, 7)            336         dropout_1[0][0]                  \n",
      "                                                                 adj_matrix[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "node_mask (InputLayer)          [(None,)]            0                                            \n",
      "__________________________________________________________________________________________________\n",
      "mask (Mask)                     (None, 7)            0           dense_convolution_1[0][0]        \n",
      "                                                                 node_mask[0][0]                  \n",
      "==================================================================================================\n",
      "Total params: 56,060\n",
      "Trainable params: 56,028\n",
      "Non-trainable params: 32\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualization Training "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "with plt.style.context(['science', 'no-latex']):\n",
    "    fig, axes = plt.subplots(1, 2, figsize=(15, 5))\n",
    "    axes[0].plot(his.history['acc'], label='Train accuracy')\n",
    "    axes[0].plot(his.history['val_acc'], label='Val accuracy')\n",
    "    axes[0].legend()\n",
    "    axes[0].set_title('Accuracy')\n",
    "    axes[0].set_xlabel('Epochs')\n",
    "    axes[0].set_ylabel('Accuracy')\n",
    "\n",
    "\n",
    "    axes[1].plot(his.history['loss'], label='Training loss')\n",
    "    axes[1].plot(his.history['val_loss'], label='Validation loss')\n",
    "    axes[1].legend()\n",
    "    axes[1].set_title('Loss')\n",
    "    axes[1].set_xlabel('Epochs')\n",
    "    axes[1].set_ylabel('Loss')\n",
    "    \n",
    "    plt.autoscale(tight=True)\n",
    "    plt.show()    "
   ]
  }
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